CN113490221B - Sustainable wireless sensor network system construction method based on heuristic algorithm - Google Patents

Sustainable wireless sensor network system construction method based on heuristic algorithm Download PDF

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CN113490221B
CN113490221B CN202110755857.4A CN202110755857A CN113490221B CN 113490221 B CN113490221 B CN 113490221B CN 202110755857 A CN202110755857 A CN 202110755857A CN 113490221 B CN113490221 B CN 113490221B
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CN113490221A (en
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李文军
张思扬
张经宇
何施茗
吴志强
王进
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Changsha University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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Abstract

The invention discloses a method for constructing a sustainable wireless sensor network system based on a heuristic algorithm, which comprises the following steps: step 1, inputting a group of candidate position nodes S loc A set of fixed terminal nodes S ter A sink node s, a parameter k, a wireless communication radius R com And a wireless charging radius R cha (ii) a Step 2, constructing a graph G based on the input; step 3, for each fixed terminal node, searching the shortest path of the sink node on the G from the fixed terminal node, and setting P as a path set of the paths; step 4, finding the minimum set D (charging node set) of the candidate position nodes, enabling each terminal node u to have at least one relay node v to belong to D, and enabling Dist (u, v) to be less than or equal to R com (ii) a Step 5, if V rel The number of vertices in (b) is greater than the repeater number limit (parameter k), then V is reduced using a local search method rel ＝V loc The number of relay nodes of n (D @ (U @) (P)).

Description

Sustainable wireless sensor network system construction method based on heuristic algorithm
Technical Field
The invention relates to the Internet of things, in particular to a sustainable wireless sensor network system construction method based on a heuristic algorithm.
Background
With the improvement of the intelligence level of the manufacturing industry, sensors or intelligent devices with sensing, monitoring, data transmission and simple calculation functions are beginning to be applied to industrial production, so that the development of industrial internet of things is promoted, a large number of problems and challenges are brought, and the attention of a large number of engineers and researchers is brought. Wireless sensor networks hold an important position in various components of the IoT (internet of things). In recent years, as the technology matures, the 5G network gradually replaces the 4G network due to its advantages of high speed, large capacity, low delay, etc., but it has its own disadvantages. Compared with 4G signals, the transmission distance of 5G signals is shorter, and is generally only 300-400 m. Therefore, the coverage of the 5G base station is smaller than that of the 4G base station. A reasonable approach to solve this problem is to deploy a large number of 5G base stations. However, the cost of building a large number of base stations is high. Therefore, how to construct a wireless sensor network system at the lowest cost in a 5G environment becomes an urgent problem to be solved.
Therefore, the invention provides a sustainable wireless sensor network system construction method based on a heuristic algorithm.
Disclosure of Invention
In order to realize the purpose of the invention, the following technical scheme is adopted for realizing the purpose:
a sustainable wireless sensor network system construction method based on heuristic algorithm is disclosed, wherein: the sustainable wireless sensor network system based on the heuristic algorithm comprises a plurality of fixed terminals, a sink node and a plurality of repeaters, wherein the repeaters can transmit data and wirelessly charge the terminals; the method comprises the following steps:
step 1, inputting a group of candidate position nodes S loc A set of fixed terminal nodes S ter A sink node s, a parameter k, a wireless communication radius R com And a wireless charging radius R cha ；
Step 2, constructing a graph G based on the input;
step 3, for each fixed terminal node, searching the shortest path of the sink node on the G from the fixed terminal node, and setting P as a path set of the paths;
step 4, finding the minimum set D (charging node) of the candidate position nodesPoint set), each terminal node u has at least one relay node v e D, Dist (u, v) is less than or equal to R com ；
Step 5, if V rel The number of vertices in (b) is greater than the repeater number limit (parameter k), then V is reduced using a local search method rel ＝V loc The number of relay nodes of n (D @ (U @) (P)).
The method, wherein the step 2 comprises:
constructing an undirected graph G (V) with edge weighting and coloring loc ∪V ter ∪{V sink })，E ＝(E grey ∪E black ) ) as follows:
(a) for S loc Creating a candidate position vertex v for each candidate position node a in a And is added to V loc The preparation method comprises the following steps of (1) performing;
(b) for S ter Creating a terminal vertex v for each terminal node a in (1) b And adding V ter ；
(c) For a sink node s, a sink vertex v is created sink ；
(d) For each terminal node a and each candidate position node b, if Dist (a, b) ≦ R cha Then a black border is created (v) a ,v b ) Weight c-Dist (v) a ,v b ) q Is added to E grey ；
(e) For each candidate location node a and each candidate location (or terminal) node b, if Dist (v) a ,v b )≤R com And there is no black border between them, a gray border (v) is created a , v b ) Weight c-Dist (v) a ,v b ) q Is added to E black 。
The method, wherein the step 3 comprises: for each vertex V ∈ V ter The algorithm uses the Dijsktra algorithm to find the shortest path from V to the convergence vertex.
The method, wherein the step 4 comprises: dividing the set of vertices D into two sets D 1 And D 2 Wherein
Figure BDA0003147383280000031
Comprises V ter All the vertexes which are adjacent to a certain vertex through the black edge; let G black ＝((V 1 ,V 2 )，E black ) Is a set of edges E black Derived graph, where V 1 ＝V ter ,V 2 ＝ V(E black )\V 1 ，G black Is a two-part graph which is composed of a plurality of parts,
Figure BDA0003147383280000032
let V 2 * ＝V 2 \D 1 ,V 1 * ＝ V ter \N Gblack (D 1 ) In which N is Gblack (D 1 ) Is D 1 Is in G black A set of vertices in (1); let G * ＝(V * ＝(V 1 * ,V 2 * )，E * ) Is formed by a set of vertices V * Derived G black A subgraph of (1); finding a minimum set using a greedy algorithm
Figure BDA0003147383280000033
At G * Middle domination of V 1 * All nodes of (2), set of vertices D 2 Consisting of all these selected vertices.
The method, wherein the step 5 comprises: if | V rel ＝V loc ∩(D∪V(P))|>k, set forth diagram G P For the union of all paths in P, if the vertex V ∈ V rel D is G P Coating the second-degree vertex of the middle part as black; let v be P (a, v) in path P sink ) A black vertex above, V is a at P (a, V) sink ) Two neighbors of (1), where v + ，v - Are each at G P Paths P (a, v) and P (v, v) sink ) If (v) is + ,v - ) E.g. E. By inclusion of (v) in P + ,v,v - ) Is used in all paths of (v) + ,v - ) Substitution subpath (v) + ,v,v - ) (ii) a This operation is repeated until | V rel K is less than or equal to | k; if there is a vertex u e (V (P) \ V) ter ) So that (u, v) + ) E, then include P (v) in P + ， v sink ) All paths of (v) + U) and P (u, v) sink ) Instead of the subpath P (v) + ，v + ) E, wherein P (v) + ，v sink )，P(u，v sink ) Are each G P From V + To v sink And from u to v sink A path of (a); an evaluation index is defined as
Figure BDA0003147383280000041
Wherein Δ cost (v, u) and Δ relay (v, u) is the inclusion of (P (v) in P + ，v sink ) Will sub-path P (v) of all paths + ，v sink ) Replacement by (v) + ，u)∪P(u，v sink ) Then, choose the minimum value of sigma (v) among all the vertices in B 0 ) Vertex v of 0 And performing a path replacement operation as a local search rule for each step in the algorithm G-SC until a set of relay vertices of at most k is found.
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FIG. 1 is a schematic diagram of a sustainable wireless sensor network system based on heuristic algorithm;
FIG. 2 is a schematic diagram of an example communication and charging network;
fig. 3 is a simulation experiment result of the CPG algorithm.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings of fig. 1-3.
As shown in fig. 1, the sustainable wireless sensor network system based on the heuristic algorithm includes a plurality of fixed terminals, a sink node and a plurality of repeaters. The repeater can transmit data and wirelessly charge the terminal. The following two conditions are satisfied, namely 1) communication conditions that the data of each terminal can be transmitted to a sink node through some relays; 2) conditions for sustainable development: each terminal may be charged by at least one repeater. The first condition ensures the basic function of the system, i.e. the communication function. The second condition ensures a sustainable operation of the whole system.
The construction of a sustainable wireless sensor network system requires two tasks to be completed, namely (1) the deployment task of a repeater at a limited position; and (2) a routing task (for each terminal, selecting a transmission route from the terminal to the sink node). The optimization goal of the system is to minimize communication costs while meeting communication conditions and sustainability conditions.
For the deployment of relays, the system should focus on the number of relays and the candidate locations to deploy the relays. Because repeaters can be expensive, it is often reasonable to limit the number of repeaters. Since the environment may be complex, e.g. adjacent to lakes, rivers, and the application scenario has some limitations, situations may arise where repeaters cannot be deployed somewhere. This means that the candidate locations to deploy the repeater may be limited.
Due to the power consumption of wireless communication, the repeater and the terminal must remain powered on all the time to receive signals, which consumes power. In addition to this energy consumption, there is also an energy loss rate during radio frequency transmission. The attenuation of energy increases with increasing distance. In the system of this patent, we only consider the energy loss during the radio frequency transmission, since it is much larger than the energy consumption when the repeater or terminal remains powered on. The relation between the communication energy consumption EC and the distance d can be expressed as a simple energy consumption function, namely EC (d) ═ c × d q Where c and q are constants and the value of c is determined by a physical parameter of the material, 2<＝q<4. Since q is not less than 2, d q >d 1 q +d 2 q Wherein the total distance d ═ d 1 +d 2 ，d 1 Is the distance between the signal and the repeater, d 2 Is the distance between the repeater and the base station. With such a simple energy consumption function, the repeater can be used not only to extend the wireless transmission range but also to reduce the communication cost.
For wireless charging, there is an effective distance, called the charging radius R, between the repeater and the charging terminal of the fixed terminal cha . Its value is determined by the type and environment of the repeater. In our proposed system, we assume the charging half of all repeatersThe diameters are the same. The charging radius is generally smaller than the wireless communication radius. In addition, in the wireless charging process, besides radio frequency transmission, a circuit for converting radio frequency into energy is also included, and the energy loss rate is usually not lower than 50%, so that the energy loss rate is very high. With the equipment known, the total energy loss rate may be greater than 80% at a charging distance of 5 meters. Therefore, in the sustainable wireless sensor network system of the present patent, it is reasonable to plan one-hop charging.
For the network in our proposed system we assume a fixed aggregation node s which connects the internal communication network with the external internet. Each repeater or terminal may receive and transmit radio frequency signals. In the real world, due to noise and energy loss, the distance between the radio frequency transmitter and receiver cannot be greater than a threshold, called the communication radius. We assume that the communication radius of the terminal, relay and receiver are the same. For wireless communications, the communication radius typically does not exceed 200 meters. For wireless charging, the radius does not exceed 20 meters. The exact value of the radius is, of course, determined by the type of repeater or terminal, the environment and the application itself. In view of the diversity of terminals, we assume that there is no communication and charging between terminals. However, repeater a can communicate with any repeater or terminal b if Dist (a, b) ≦ R com Where Dist (a, b) is the distance between a and b, R com Is the wireless communication radius.
Fig. 2 is an example of a communication and charging network in the system. In the network, c, e, f and g are deployed relay nodes. We omit all candidate location nodes except the deployed relay nodes. a. b and d are fixed terminal nodes. h is a sink node.
Radius of charge R cha 15m, communication radius R com 25 m. Paths a → c → g → h, b → c → g → h, d → f → g → h are the effective communication paths of the fixed terminal nodes a, b, d to h, respectively. At this time, the terminal c can charge a and b, because Dist (a, c) is Dist (b, c) 10m ≦ R cha . Relay f cannot charge terminal d because Dist (d, f) is 25m>R cha Route d → e → g → h, Dist (e, g) ═ 50m>R com It is aAn invalid communication path. Therefore, in this case, the sustainable wireless sensor network system must use at least 4 repeaters, although only 3 repeaters are used for communication.
Because the positions of the fixed terminal and the sink node are determined, the problem of location selection of the relay is mainly solved when a sustainable wireless sensor network system is built.
Suppose a scenario of a communication system is a three-dimensional space (R) 3 )。S ter Is a group of fixed terminal nodes, s is a fixed sink node, and aims to deploy a group of relay nodes, so that: (1) for each terminal node v ∈ S ter There is one relay node u ∈ S rel And Dist (u, v) is less than or equal to R cha ，S rel Represents a set of repeater nodes; (2) for each terminal node v ∈ S ter There is a communication path P from v to the sink node s v (one routing path) so that P v The distance between any two adjacent nodes is not more than R com (ii) a (3) Total communication overhead (energy consumption)
Figure BDA0003147383280000071
Is at a minimum, wherein
Figure BDA0003147383280000072
In this function, there is an implicit assumption about the total communication cost function that each terminal operates at the same frequency.
The problem of sustainable communication: given distribution in three-dimensional space R 3 A group of fixed terminal nodes S ter A set of candidate location nodes S loc And a sink node S, wherein a communication cost function is EC, and the wireless communication radius and the wireless charging radius are R respectively com Radius R cha Selecting a maximum of k candidate node positions (the selected candidate position nodes are used for deploying relay nodes) as much as possible, so that:
(1) for each fixed terminal node v ∈ S ter One Dist (u, v) is less than or equal to R cha The relay node u of (1);
(2) for each terminal node v ∈ S ter There is a path P from v to the sink node s through the relay node v In which P is v The distance between any two adjacent nodes is not more than R com ；
(3) The total communication cost is minimal.
The technical scheme for solving the problems is as follows: inputting a set of candidate location nodes S loc A set of fixed terminal nodes S ter A sink node s, a parameter k, a wireless communication radius R com And a wireless charging radius R cha The algorithm is as follows (in this context, vertex V and edge E refer to a concept in graph theory):
(A) constructing a graph G based on the input instance of the question;
(B) for each fixed terminal node, finding from it the shortest path of the sink nodes on G (assuming P is the set of paths for these paths);
(C) finding the minimum set D (charging node set) of candidate position nodes, enabling each terminal node u to have at least one relay node v E D, and enabling Dist (u, v) to be less than or equal to R com ；
(D) If repeater location vertex V rel The number of vertices in (b) is greater than the repeater number limit (parameter k), then V is reduced using a local search method rel ＝V loc Number of repeater nodes (V) of n (D $ V (P)) n loc Vertices representing candidate location nodes).
The whole algorithm can be divided into two steps. The first step, consisting of steps (a) (B) (C), is a subroutine for selecting a set of candidate location nodes that satisfy communication conditions and sustainability conditions, but may not satisfy the relay number limit. Algorithm 1 (called CPG) expresses this process.
The second step (D) is to reserve k by two different algorithms (Algorithm 2 and Algorithm 3), called P-SC (Algorithm 2) and G-SC (Algorithm 3), respectively.
Therefore, the first algorithm we solve the problem consists of CPG and P-SC. The second consists of CPG and G-SC.
Now, details of steps (a) to (D) of the algorithm are described in detail.
1) Step (A):
constructing an undirected graph G (V) with edge weighting and coloring loc ∪V ter ∪{V sink })，E ＝(E grey ∪E black ) Is as follows (wherein V) ter Is a vertex, V, representing the location of the terminal device sink Being vertices representing positions of the receiving ends, also called converging vertices, E grey Is a gray edge, E black Is a black border):
(a) for S loc Creating a candidate position vertex v for each candidate position node a in a And is added to V loc Performing the following steps;
(b) for S ter Creating a terminal vertex v for each terminal node a in (1) b And adding V ter ；
(c) For a sink node s, a sink vertex v is created sink ；
(d) For each terminal node a and each candidate position node b, if Dist (a, b) ≦ R cha Then a black border is created (v) a ,v b ) Weight c-Dist (v) a ,v b ) q Is added to E grey ；
(e) For each candidate location node a and each candidate location (or terminal) node b, if Dist (v) a ,v b )≤R com And there is no black border between them, a gray border (v) is created a , v b ) Weight c-Dist (v) a ,v b ) q Is added to E black 。
2) Step (B) of assigning V e V to each vertex ter The algorithm uses the Dijsktra algorithm to find the shortest path from V to the convergence vertex.
3) Step (C) of finding a V ter And makes it as small as possible, the charging node set D (i.e., the relay node). In our algorithm, the set of vertices D can be divided into two sets D 1 And D 2 Wherein
Figure BDA0003147383280000091
Comprises V ter Middle pointAll vertices where the vertices are adjoined by a black edge. In the following, we show just how to find the set of vertices D 2 . Let G black ＝ ((V 1 ,V 2 )，E black ) Is a set of edges E black Derived graph, where V 1 ＝V ter ,V 2 ＝V (E black )\V 1 . From the construction of G we know that G black Is a two-part graph which is composed of a plurality of parts,
Figure BDA0003147383280000102
let V 2 * ＝V 2 \D 1 ,V 1 * ＝V ter \N Gblack (D 1 ) In which N is Gblack (D 1 ) Is D 1 Is in G black Set of vertices in (1). Let G * ＝(V * ＝(V 1 * ,V 2 * )，E * ) Is formed by a set of vertices V * Derived G black Is shown in the figure. Then, we use a simple greedy algorithm to find a minimum set
Figure BDA0003147383280000101
At G * Middle domination of V 1 * All nodes of (1). Greedy strategy is from V 2 * Selecting a vertex with the maximum number of G steps until V 1 * All nodes of (2) are controlled, a set of vertices D 2 Consisting of all these selected vertices.
4) Step (D) if | V rel ＝V loc ∩(D∪V(P))|>k, then we must reduce V rel Until it is no greater than k. To achieve this goal, we have two different approaches (two different local search rules). Two different heuristic algorithms are introduced, namely an algorithm P-SC (algorithm 2) and an algorithm G-SC (algorithm 3). Set up the drawing G P Is the union of all paths in P. If the vertex V ∈ V rel D is G P The second vertex in (1), we paint it black. The strategy of our local search rules is to remove under communication conditions and sustainability conditionsAnd a black vertex. Let v be P (a, v) in path P sink ) A black vertex above, V is a at P (a, V) sink ) Two neighbors of (1), where v + ，v - Are each at G P Paths P (a, v) and P (v, v) sink ) The above. In the first rule, we consider the case (v) + ,v - ) E.g. E. If so, we include (v) in P + ,v,v - ) Is used in all paths of (v) + ,v - ) Substituted sub-path (v) + ,v,v - )。
After such an operation has been carried out, V rel The number of vertices in (1) is reduced. The algorithm P-SC performs this operation repeatedly until | V rel And (5) less than or equal to k. In the second rule, we refine the selection of black vertices as follows. A local search between any two nodes in the entire graph is found. However, the longer the runtime, the lower the communication cost. For the algorithm P-SC, the total run time is (O (| V 3 )). The running time of the algorithm G-SC is (O (| E | | V |) 3 ) Greater than the run time of the P-SC. If there is a vertex u e (V (P) \ V) ter ) So that (u, v) + ) E.e, then we include P (v) in P + ，v sink ) All paths of (v) + U) and P (u, v) sink ) Instead of the subpath P (v) + ，v + ) E, wherein P (v) + ，v sink )，P(u，v sink ) Are each G P From V + To v sink And from u to v sink The path of (2). Such implementation of operations causes an increase in communication costs, but v rel The number of vertices in (1) is increased by at least 1. For the greedy strategy, we define an evaluation index as
Figure BDA0003147383280000111
Wherein Δ cost (v, u) and Δ relay (v, u) is the inclusion of (P (v) in P + ，v sink ) Will sub-path P (v) of all paths + ，v sink ) Replacement by (v) + ，u)∪P(u，v sink ) Then, in order to make the increment as small as possible, the increment of the communication cost and the decrement of the number of relay vertices are set to all the vertices in BIs chosen to have a minimum value ∑ (v) 0 ) Vertex v of 0 And performing a path replacement operation as a local search rule for each step in the algorithm G-SC until a set of relay vertices of at most k is found.
A. Performance analysis
As described above, the function of energy consumption (communication cost) is ec (d) kd n N is more than or equal to 2 and less than or equal to 4. For simplicity, we set ec (d) ═ d in the experiment 2 . In consideration of practical situations, let us set the wireless communication radius R com 40m, wireless charging radius R cha 15. Each experiment was performed 1000 times and the values for each result were averaged. All terminals, candidate locations and receptions are randomly generated in a limited three-dimensional space.
The results of simulation experiments of the CPG algorithm are given in fig. 3. There are four different simulation scenes, the sizes of which are 100m × 10m, 150m × 15m, 200m × 20m and 250m × 25m, and the number of nodes at the candidate positions in the scenes is 100, 225, 400 and 625. In the scenario, the number of end nodes has 5 different values, 10, 20, 30, 40 and 50 respectively. It is not difficult to see that the communication cost and the number of charging nodes increase as the number of terminal nodes increases. The reason is that the total communication cost is related to the number of communication paths, which is determined by the number of terminal nodes.
The invention designs a sustainable wireless sensor network communication system which can maintain the characteristic of sustainable communication by carrying out relay charging on a terminal.

Claims (1)

1. A method for constructing a sustainable wireless sensor network system based on a heuristic algorithm is characterized by comprising the following steps: the sustainable wireless sensor network system based on the heuristic algorithm comprises a plurality of fixed terminals, a sink node and a plurality of repeaters, wherein the repeaters can transmit data and wirelessly charge the terminals; the method comprises the following steps:
step 1, inputting a group of candidate position nodes S loc A set of fixed terminal nodes S ter A sink node s, a parameter k, a wireless communication radius R com And a wireless charging radius R cha Where k represents the repeater number limit;
step 2, constructing a graph G based on the input, comprising: constructing an undirected graph G (V) with edge weighting and coloring loc ∪V ter ∪{V sink })，E＝(E grey ∪E black ) ) as follows:
(a) for S loc Creating a candidate position vertex v for each candidate position node a in a And is added to V loc Performing the following steps;
(b) for S ter Creating a terminal vertex v for each terminal node a in (1) b And adding V ter ；
(c) For a sink node s, a sink vertex v is created sink ；
(d) For each terminal node a and each candidate position node b, if Dist (a, b) ≦ R cha Then a black border is created (v) a ,v b ) Weight c DISt (v) a ,v b ) q Is added to E grey ；
(e) For each candidate location node a and each candidate location or terminal node b, if Dist (v) a ,v b )≤R com And there is no black border between them, a gray border (v) is created a ,v b ) Weight c DISt (v) a ,v b ) q Is added to E black ；
Step 3, for each fixed terminal node, searching the shortest path of the sink node on the G from the fixed terminal node, and setting P as a path set of the paths;
step 4, finding a charging node set of a minimum set D of candidate position nodes, enabling each terminal node u to have at least one relay node v to belong to D, and enabling Dist (u, v) to be less than or equal to R com The method comprises the following steps: dividing the set of vertices D into two sets D 1 And D 2 Wherein
Figure FDA0003523159310000021
Comprises V ter All the vertexes which are adjacent to a certain vertex through the black edge; let G black ＝((V 1 ,V 2 )，E black ) Set E for edges black Derived graph, where V 1 ＝V ter ,V 2 ＝V(E black )\V 1 ，G black Is a two-part graph which is composed of a plurality of parts,
Figure FDA0003523159310000022
let V 2 * ＝V 2 \D 1 ,V 1 * ＝V ter \N Gblack (D 1 ) In which N is Gblack (D 1 ) Is D 1 Is in G black A set of vertices in (1); let G * ＝(V * ＝(V 1 * ,V 2 * )，E * ) Is formed by a set of vertices V * Derived G black A subgraph of (1); finding a minimum set using a greedy algorithm
Figure FDA0003523159310000023
At G * Middle domination of V 1 * All nodes of (2), set of vertices D 2 Consists of all these selected vertices;
step 5, if the repeater position vertex V rel The number of vertices in (1) is greater than the number limit k of repeaters, then V is reduced using a local search method rel ＝V loc The number of relay nodes of n (D { (P) }) includes: if | V rel ＝V loc ∩(D∪V(P))|>k, set forth diagram G P For the union of all paths in P, if the vertex V ∈ V rel D is G P Coating the second-degree vertex of the middle part as black; let v be P (a, v) in path P sink ) A black vertex above, V is a at P (a, V) sink ) Two neighbors of (1), where v + ，v - Are each at G P Paths P (a, v) and P (v, v) sink ) If (v) is + ,v - ) E, using P to contain (v) + ,v,v - ) All paths of (v) are + ,v - ) Substitution subpath (v) + ,v,v - ) (ii) a This operation is repeated until | V rel K is less than or equal to | k; if there is a vertex u e(V(P)\V ter ) So that (u, v) + ) E.e, then P (v) is contained in P + ，v sink ) All paths of (v) + U) and P (u, v) sink ) Instead of the subpath P (v) + ，v + ) E, wherein P (v) + ，v sink )，P(u，v sink ) Are each G P From V + To v sink And from u to v sink A path of (a); an evaluation index is defined as
Figure FDA0003523159310000024
Wherein Δ cost (v, u) and Δ relay (v, u) is the inclusion of (P (v) in P + ，v sink ) Will sub-path P (v) of all paths + ，v sink ) Replacement by (v) + ，u)∪P(u，v sink ) Then, choose the minimum value of sigma (v) among all the vertices in B 0 ) Vertex v of 0 Performing path replacement operation on each step in the algorithm G-SC as a local search rule until a relay vertex set with the size of at most k is found; wherein V loc And V (P) represents a vertex of the shortest path set P of the aggregation node.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106993320A (en) * 2017-03-22 2017-07-28 江苏科技大学 Wireless sensor network cooperation transmission method for routing based on many relay multi-hops
CN107835499A (en) * 2017-10-26 2018-03-23 河海大学常州校区 A kind of mobile charging method based on sub-clustering and energy relay in WSNs

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CN111770467A (en) * 2020-06-19 2020-10-13 昆明理工大学 Efficient offline scheme for cooperative movement energy supplement of sensor and actuator

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106993320A (en) * 2017-03-22 2017-07-28 江苏科技大学 Wireless sensor network cooperation transmission method for routing based on many relay multi-hops
CN107835499A (en) * 2017-10-26 2018-03-23 河海大学常州校区 A kind of mobile charging method based on sub-clustering and energy relay in WSNs

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